User Graph Regularized Pairwise Matrix Factorization for Item Recommendation
Lecture notes in computer sciencePublished 1 January 2011
Liang Du, Xuan Li, Yi-Dong Shen
Citations45
SJR quartileQ2
SJR score0.35
SNIP0.55
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TL;DR
This paper proposes a novel method, called User Graph regularized Pairwise Matrix Factorization (UGPMF), to seamlessly integrate user information into pairwise matrix factorization procedure, and is able to find more compact, low dimensional representations for users and items.
Abstract
Item recommendation from implicit, positive only feedback is an emerging setup in collaborative filtering in which only one class examples are observed. In this paper, we propose a novel method, called User Graph regularized Pairwise Matrix Factorization (UGPMF), to seamlessly integrate user information into pairwise matrix factorization procedure. Due to the use of the available information on user side, we are able to find more compact, low dimensional representations for users and items. Experiments on real-world recommendation data sets demonstrate that the proposed method significantly outperforms various competing alternative methods on top-k ranking performance of one-class item recommendation task.
Keywords
Computer ScienceDecision Sciences
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